One Person + AI Agents vs 10-Person Dev Team: Who Actually Wins?

I ran an experiment. I gave the same project specification to a 10-person development team at a company I consult for and to a single architect (me) working with AI agents. The spec was a project management SaaS with real-time collaboration, role-based access, reporting dashboards, and Stripe billing.

The team delivered in fourteen weeks.

I delivered in nineteen days.

Both products went to production. Both handle real users. The code quality metrics (test coverage, type safety, Lighthouse scores) were comparable. The one-person build actually scored higher on two out of three metrics.

This is not a flex. It is data. And the data tells a story about how software development works in 2026.

The 10-Person Team

The team consisted of: a tech lead, three senior developers, two mid-level developers, one junior developer, a product manager, a designer, and a QA engineer.

Their process was standard agile. Two-week sprints. Daily standups. Sprint planning on Mondays. Retrospectives on Fridays. Code reviews required for every pull request. The tech lead spent roughly half their time in meetings.

The timeline broke down like this:

Weeks 1-2: Sprint planning, architecture discussions, design phase. One sprint spent mostly in meetings defining the approach.

Weeks 3-6: Core feature development. Three developers working on different features simultaneously, with the tech lead reviewing and resolving integration conflicts.

Weeks 7-9: Secondary features, integration work, resolving technical debt accumulated during the rush in weeks 3-6.

Weeks 10-12: Testing, bug fixing, performance optimization.

Weeks 13-14: Final fixes, staging deployment, production launch.

Total cost at market rates: approximately 140,000 EUR in loaded salary costs for the 14-week engagement.

The One-Person Build

Day 1: Architecture. Defined the data models, API contracts, component hierarchy, and deployment architecture. Wrote the CLAUDE.md that would guide the AI agents.

Days 2-3: Foundation. AI agents scaffolded the project, set up authentication with Clerk, configured the Convex backend, and built the design system.

Days 4-8: Core features. Task boards, real-time collaboration, role-based access control. I specified each feature at a medium level of abstraction. The agents implemented. I reviewed, adjusted architectural decisions, and moved to the next feature.

Days 9-12: Secondary features. Reporting dashboards, user settings, notification system, Stripe billing integration.

Days 13-15: Testing and polish. The testing agent ran security, performance, accessibility, and responsive tests. Issues found were fixed and retested.

Days 16-19: Final polish, deployment configuration, documentation, production launch.

Total cost: approximately 12,000 EUR in service fees and infrastructure.

Why the Solo Build Was Faster

The speed difference was not because I am a faster typer or because I worked longer hours. I worked standard days. The speed came from four structural advantages.

Zero coordination overhead. The 10-person team spent approximately 20% of their collective time in meetings. Standups, sprint planning, design reviews, code review discussions, retrospectives. For 10 people over 14 weeks, that is roughly 1,120 hours of meetings. I had zero meetings with myself.

No integration conflicts. When three developers build features simultaneously, their code eventually needs to merge. Merge conflicts, architectural disagreements, and inconsistent approaches create friction. AI agents working from a single specification produce code that integrates cleanly.

No knowledge transfer delay. When the team's frontend developer needed to understand how the backend developer structured the API, they scheduled a meeting. When I needed to understand any part of the codebase, I already knew because I had specified it.

No context switching. Each developer on the team was handling multiple responsibilities: coding, reviewing others' code, attending meetings, responding to Slack. I spent 90% of my time on the highest-leverage activity: making architectural decisions and directing agents.

Where the 10-Person Team Had Advantages

This comparison is not entirely one-sided. The traditional team had genuine advantages in certain areas.

Diverse perspectives. Having 10 people discuss a feature surfaces edge cases and user experience considerations that a single person might miss. The team caught a UX issue in the reporting dashboard that I did not consider until a user reported it post-launch.

Continuous coverage. With 10 people, multiple features progressed simultaneously. My build was sequential.

Institutional knowledge. The team built collective understanding of the codebase. My build created a single point of knowledge (me). This is a real risk that solo builders need to mitigate with thorough documentation.

The Quality Comparison

I tracked five quality metrics across both builds.

Metric	Solo Build	Team Build
Test coverage	91%	72%
TypeScript strict compliance	100%	100%
Lighthouse performance	96	88
Accessibility (WCAG 2.1 AA)	98%	82%
Production bugs (first month)	3	11

AI agents generate tests automatically. Human teams write them reluctantly. The testing agent checked every component against accessibility standards. The team relied on developer awareness, which varied.

The Cost Comparison

Direct costs over the first year:

10-person team: ~580,000 EUR (14 weeks of build at 140K + 9.5 months of ongoing maintenance and feature development at ~46K/month).

Solo + AI agents: ~72,000 EUR (19 days of build at 12K + 11 months of ongoing CTO partnership at ~5,500/month).

The solo model costs 12% of the traditional model and delivered faster with comparable or better quality metrics.

For a broader analysis of AI agency versus traditional team cost comparisons, the numbers tell the same story at different scales.

When Each Model Works Best

The one-person plus AI agents model excels when: you need to move fast, you have limited capital, the product is in a well-understood domain, and the scope is achievable by one architectural mind.

The 10-person team model excels when: you are operating at scale with millions of users, you need 24/7 operational coverage, the domain requires deep specialist expertise, or the organization's culture and processes require collaborative development.

For the vast majority of startups and SMBs, the solo model is overwhelmingly better. You get faster delivery, lower cost, higher quality, and the freedom to iterate rapidly without coordinating across a team.

The Uncomfortable Conclusion

The 10-person team was not bad. They were competent, professional, and well-organized. They followed best practices. They ran a good process.

They still lost on every metric except parallel development capacity and institutional knowledge.

This is not a failure of the team. It is a structural disadvantage of the team model in an era where AI agents handle execution. When the execution layer is automated, the coordination overhead of a large team becomes pure cost with no corresponding benefit.

One person making excellent decisions, amplified by AI agents that execute flawlessly, produces more than ten people making good decisions individually but losing productivity to coordination.

The economics are clear. The question is not whether one person plus AI agents can match a team. The question is why you would choose the team model when the alternative exists.

Sources

• Brooks' Law and Software Team Productivity - Coordination overhead research
• Stack Overflow: Developer Productivity Survey 2025 - Meeting overhead and context-switching data
• GitHub: The State of the Octoverse 2025 - Code quality and test coverage benchmarks